{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ======基于Tensorflow的basic word2vec===========\n",
    "\"\"\"Basic word2vec example.\"\"\"\n",
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import collections\n",
    "import math\n",
    "import os\n",
    "import random\n",
    "from tempfile import gettempdir\n",
    "import zipfile\n",
    "\n",
    "import numpy as np\n",
    "from six.moves import urllib\n",
    "from six.moves import xrange  # pylint: disable=redefined-builtin\n",
    "import tensorflow as tf\n",
    "#下面是为了解决matplotlib显示中文的问题\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False #用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data size 1903073\n"
     ]
    }
   ],
   "source": [
    "# 读取文件，将文件中的句子转成词汇列表\n",
    "filename = './QuanSongCi.txt'                      # 文件名\n",
    "def read_data(filename):\n",
    "    with open(filename, encoding=\"utf-8\") as f:\n",
    "        data = f.read()\n",
    "        data = list(data)\n",
    "    return data\n",
    "\n",
    "vocabulary = read_data(filename)\n",
    "print('Data size', len(vocabulary))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2: Build the dictionary and replace rare words with UNK token.\n",
    "vocabulary_size = 5000\n",
    "words = vocabulary\n",
    "n_words = vocabulary_size - 1\n",
    "count = [['UNK', -1]]\n",
    "count.extend(collections.Counter(words).most_common(n_words - 1)) \n",
    "dictionary = dict()\n",
    "for word, _ in count:\n",
    "    dictionary[word] = len(dictionary) \n",
    "for word in words[:20]:\n",
    "    index = dictionary.get(word,0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = list()\n",
    "unk_count = 0\n",
    "for word in words: \n",
    "    index = dictionary.get(word, 0) \n",
    "    if index == 0:   \n",
    "        unk_count += 1\n",
    "    data.append(index)\n",
    "count[0][1] = unk_count\n",
    "reversed_dictionary = dict(zip(dictionary.values(), dictionary.keys()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=128 #8\n",
    "num_skips=2;skip_window=1\n",
    "data_index = 0\n",
    "global data_index \n",
    "assert batch_size % num_skips == 0 \n",
    "assert num_skips <= 2 * skip_window\n",
    " \n",
    "batch = np.ndarray(shape=(batch_size), dtype=np.int32)\n",
    "labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)\n",
    "\n",
    "span = 2 * skip_window + 1\n",
    "buffer = collections.deque(maxlen=span)\n",
    " \n",
    "if data_index + span > len(data):\n",
    "    data_index = 0  \n",
    "buffer.extend(data[data_index : data_index+span]) \n",
    "data_index += span                                 \n",
    "\n",
    "for i in range(batch_size // num_skips):  \n",
    "    context_words = [w for w in range(span) if w != skip_window] \n",
    "    \n",
    "    words_to_use = random.sample(context_words,num_skips) \n",
    "    \n",
    "    for j,context_word in enumerate(words_to_use):   \n",
    "        batch[i * num_skips + j] = buffer[skip_window]\n",
    "        labels[i * num_skips + j,0] = buffer[context_word] \n",
    "    \n",
    "    if data_index == len(data):\n",
    "        buffer.extend(data[0:span]) \n",
    "        data_index = span \n",
    "    else:\n",
    "        buffer.append(data[data_index])\n",
    "        data_index += 1\n",
    "\n",
    "data_index = (data_index + len(data) - span) % len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "embedding_size = 128  # Dimension of the embedding vector.\n",
    "skip_window = 1       # How many words to consider left and right.\n",
    "num_skips = 2         # How many times to reuse an input to generate a label.\n",
    "num_sampled = 64      # Number of negative examples to sample.\n",
    "# We pick a random validation set to sample nearest neighbors. Here we limit the\n",
    "# validation samples to the words that have a low numeric ID, which by\n",
    "# construction are also the most frequent. These 3 variables are used only for\n",
    "# displaying model accuracy, they don't affect calculation.\n",
    "valid_size = 16     # Random set of words to evaluate similarity on.\n",
    "valid_window = 100  # Only pick dev samples in the head of the distribution.\n",
    "valid_examples = np.random.choice(valid_window, valid_size, replace=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = tf.Graph()                               # 创建graph\n",
    "with graph.as_default():                        # 设置为默认的graph\n",
    "    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])\n",
    "    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])\n",
    "    valid_dataset = tf.constant(valid_examples, dtype=tf.int32) \n",
    "\n",
    "    with tf.device('/cpu:0'): \n",
    "        embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))\n",
    "        embed = tf.nn.embedding_lookup(embeddings, train_inputs)   \n",
    "\n",
    "        nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size],\n",
    "                            stddev=1.0 / math.sqrt(embedding_size)))  \n",
    "        nce_biases = tf.Variable(tf.zeros([vocabulary_size])) \n",
    "\n",
    "    loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights,\n",
    "                                         biases=nce_biases,\n",
    "                                         labels=train_labels,\n",
    "                                         inputs=embed,\n",
    "                                         num_sampled=num_sampled,\n",
    "                                         num_classes=vocabulary_size))\n",
    "\n",
    "    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)\n",
    "\n",
    "    norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))\n",
    "    normalized_embeddings = embeddings / norm\n",
    "    valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset) \n",
    "    similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)\n",
    "    init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Average loss at step  0 :  215.3495330810547\n",
      "Nearest to 来: 镗, 挑, 嚬, 爬, 趱, 恒, 糟, 孟,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 黔, 刺, 包, 枢, 洄, 羯, , 克,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 著, 几, 栩, 讯, 秾, :, 题, 薤,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 谐, 捺, 荃, 侠, 婪, 钩, 飨, 蜩,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 绝, 溯, 旻, 卧, 珂, 俏, 罚, 踵,\n",
      "Average loss at step  2000 :  14.162044836729764\n",
      "Average loss at step  4000 :  1.0905571313798428\n",
      "Average loss at step  6000 :  1.0259532076120377\n",
      "Average loss at step  8000 :  1.0221946882307529\n",
      "Average loss at step  10000 :  1.0127956046462059\n",
      "Nearest to 来: 别, \n",
      ", 隋, 寰, 。, 之, 分, 放,\n",
      "Nearest to 事: 宕, 铁, 矛, 分, （, 懋, 计, 投,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 。, \n",
      ", 季, 四, 障, （, 子,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, 障, 入, 分, \n",
      ", 出, 异,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 。, \n",
      ", 经, 是, 隋, 分, 映, 别,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: \n",
      ", 。, 是, 映, 四, 天, 季, 当,\n",
      "Average loss at step  12000 :  1.0145786280333995\n",
      "Average loss at step  14000 :  1.0121969988048076\n",
      "Average loss at step  16000 :  1.0111749831438064\n",
      "Average loss at step  18000 :  1.0119511878192424\n",
      "Average loss at step  20000 :  1.0059973226487637\n",
      "Nearest to 来: 别, 隋, 。, \n",
      ", 之, 是, 寰, 分,\n",
      "Nearest to 事: 宕, 铁, 矛, （, 懋, 计, 分, 投,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 。, \n",
      ", 季, 障, 四, 明, （,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, 障, \n",
      ", 分, 入, 是, 出,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 。, \n",
      ", 是, 经, 隋, 分, 映, 别,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 。, 是, \n",
      ", 映, 四, 天, 当, 季,\n",
      "Average loss at step  22000 :  1.0089758494198322\n",
      "Average loss at step  24000 :  1.0012497017085553\n",
      "Average loss at step  26000 :  1.0074786311089992\n",
      "Average loss at step  28000 :  1.0061272993385793\n",
      "Average loss at step  30000 :  1.0096311745345592\n",
      "Nearest to 来: 之, 别, 隋, 是, \n",
      ", 分, 寰, 掩,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, （, 计, 投, 袂,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 。, 季, \n",
      ", 障, 四, 是, （,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, \n",
      ", 障, 分, 是, 异, 一,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 。, \n",
      ", 是, 经, 隋, 分, 映, 掩,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, \n",
      ", 。, 映, 四, 天, 季, 当,\n",
      "Average loss at step  32000 :  1.0038276337087155\n",
      "Average loss at step  34000 :  0.9991658635139465\n",
      "Average loss at step  36000 :  1.0095908414125443\n",
      "Average loss at step  38000 :  0.9988094135820865\n",
      "Average loss at step  40000 :  1.0001872001886367\n",
      "Nearest to 来: 之, 别, 隋, 是, \n",
      ", 分, 寰, 掩,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, （, 投, 袂,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 。, 四, 障, \n",
      ", 是, （,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 。, 障, 是, 异, 一, 分,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: \n",
      ", 是, 。, 经, 隋, 分, 映, 异,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, \n",
      ", 四, 天, 映, 当, 季, 不,\n",
      "Average loss at step  42000 :  1.0016459167897702\n",
      "Average loss at step  44000 :  1.002064939379692\n",
      "Average loss at step  46000 :  0.9994292104840279\n",
      "Average loss at step  48000 :  0.9994388866126538\n",
      "Average loss at step  50000 :  1.002176449507475\n",
      "Nearest to 来: 之, 隋, 别, 是, 分, 掩, 寰, 季,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, （,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 四, 。, （, 明,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, 障, \n",
      ", 是, 异, 一, 分,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 。, 是, \n",
      ", 经, 隋, 分, 映, 异,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, \n",
      ", 经, 。, 四, 季, 映, 一,\n",
      "Average loss at step  52000 :  1.0033886699676513\n",
      "Average loss at step  54000 :  0.9983817576169968\n",
      "Average loss at step  56000 :  0.9986281163394451\n",
      "Average loss at step  58000 :  0.9970259863436222\n",
      "Average loss at step  60000 :  0.9979385553896427\n",
      "Nearest to 来: 之, 是, 隋, 别, 分, 掩, 季, 经,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 四, （, 。, 明,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, 障, \n",
      ", 是, 异, 一, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 是, 经, 。, \n",
      ", 映, 分, 隋, 异,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, 四, 映, \n",
      ", 一, 掩, 季,\n",
      "Average loss at step  62000 :  1.0014266046583653\n",
      "Average loss at step  64000 :  0.9997724113464356\n",
      "Average loss at step  66000 :  0.9962973048686982\n",
      "Average loss at step  68000 :  0.9982796506583691\n",
      "Average loss at step  70000 :  1.0006522835791112\n",
      "Nearest to 来: 之, 是, 隋, 别, 经, 季, 分, 掩,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 。, 障, 是, 四, （, \n",
      ",\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, \n",
      ", 障, 是, 异, 一, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 。, \n",
      ", 是, 经, 异, 映, 隋, 分,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, \n",
      ", 四, 。, 映, 一, （,\n",
      "Average loss at step  72000 :  1.0008995399177074\n",
      "Average loss at step  74000 :  0.9997050345540047\n",
      "Average loss at step  76000 :  0.9960567222237587\n",
      "Average loss at step  78000 :  0.9942794821560382\n",
      "Average loss at step  80000 :  1.001289290457964\n",
      "Nearest to 来: 之, 是, 隋, 经, 季, 分, 别, 掩,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 四, （, 明, 隋,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, \n",
      ", 障, 是, 异, 别, 一,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 。, 是, \n",
      ", 经, 异, 映, 分, ）,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, 四, \n",
      ", 一, 映, 不, 当,\n",
      "Average loss at step  82000 :  0.9946382183134556\n",
      "Average loss at step  84000 :  0.9955225223302842\n",
      "Average loss at step  86000 :  0.9962668513059616\n",
      "Average loss at step  88000 :  1.0017905569970609\n",
      "Average loss at step  90000 :  1.000483235269785\n",
      "Nearest to 来: 之, 经, 季, 是, 隋, 别, 分, 掩,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 。, 四, 隋, （,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 。, 障, 是, 异, 别, 长,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: \n",
      ", 是, 经, 。, 异, ）, 映, 隋,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, \n",
      ", 四, 一, 不, （, 映,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  92000 :  1.0040486652255058\n",
      "Average loss at step  94000 :  0.9970768890380859\n",
      "Average loss at step  96000 :  0.999924722135067\n",
      "Average loss at step  98000 :  0.9995954159200191\n",
      "Average loss at step  100000 :  0.9954248082637787\n",
      "Nearest to 来: 之, 经, 季, 是, 隋, 分, 掩, 别,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 是, 障, 四, 隋, （, 粤,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 。, 是, 障, 异, 别, 长,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 是, 经, \n",
      ", 异, 。, ）, 映, 分,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, 四, 一, 当, 不, 映, （,\n",
      "Average loss at step  102000 :  0.9949460099935532\n",
      "Average loss at step  104000 :  1.001329254180193\n",
      "Average loss at step  106000 :  0.9931212269365788\n",
      "Average loss at step  108000 :  0.994609403014183\n",
      "Average loss at step  110000 :  0.9979131114780903\n",
      "Nearest to 来: 之, 季, 经, 是, 隋, 分, 掩, 别,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 四, 隋, （, 粤,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 异, 。, 是, 障, 长, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: \n",
      ", 是, 异, 经, ）, 。, 映, 分,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, \n",
      ", 四, 一, 不, （, 当,\n",
      "Average loss at step  112000 :  1.0004794631004335\n",
      "Average loss at step  114000 :  0.9948579596281052\n",
      "Average loss at step  116000 :  0.9899398777782917\n",
      "Average loss at step  118000 :  0.9924762261509895\n",
      "Average loss at step  120000 :  0.9913053737580776\n",
      "Nearest to 来: 之, 经, 季, 是, 隋, 分, 掩, 翠,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 四, 隋, 。, 粤,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 。, 异, 是, 障, 长, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: \n",
      ", 经, 异, 。, 是, ）, 映, 掩,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, 四, 一, 当, 不, \n",
      ", （,\n",
      "Average loss at step  122000 :  0.9932387543916702\n",
      "Average loss at step  124000 :  0.992296200722456\n",
      "Average loss at step  126000 :  0.999045123398304\n",
      "Average loss at step  128000 :  0.9919471186995507\n",
      "Average loss at step  130000 :  1.0007015489339828\n",
      "Nearest to 来: 之, 经, 季, 是, 隋, 翠, 放, 分,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 隋, 四, 粤, 柳,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 。, 异, 是, 障, 长, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 异, \n",
      ", ）, 经, 是, 。, 映, 隋,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, 四, 一, 不, \n",
      ", 当, 之,\n",
      "Average loss at step  132000 :  0.9959527671039105\n",
      "Average loss at step  134000 :  0.9957735263109208\n",
      "Average loss at step  136000 :  0.9918146297633648\n",
      "Average loss at step  138000 :  1.003023583739996\n",
      "Average loss at step  140000 :  0.99594620475173\n",
      "Nearest to 来: 之, 季, 经, 是, 翠, 隋, ，, 分,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 是, 四, 隋, 粤, 。,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 异, 长, 。, 是, 障, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: \n",
      ", 异, ）, 经, 是, 。, 映, 掩,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, 四, \n",
      ", 不, 一, 当, 映,\n",
      "Average loss at step  142000 :  0.9954339787065983\n",
      "Average loss at step  144000 :  0.9946417140960694\n",
      "Average loss at step  146000 :  0.998317585170269\n",
      "Average loss at step  148000 :  0.9965544453561306\n",
      "Average loss at step  150000 :  0.9996489538550377\n",
      "Nearest to 来: 之, 经, 季, 是, 隋, 翠, 分, 放,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 季, 障, 。, 是, 隋, 四, 粤,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, 。, 异, \n",
      ", 长, 是, 障, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 异, ）, 。, 经, 是, \n",
      ", 映, 分,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, 四, 不, 一, 当, 几, 隋,\n",
      "Average loss at step  152000 :  0.9945681083798409\n",
      "Average loss at step  154000 :  0.9979453774392605\n",
      "Average loss at step  156000 :  0.9917646954059601\n",
      "Average loss at step  158000 :  1.0032211163043976\n",
      "Average loss at step  160000 :  0.991968338817358\n",
      "Nearest to 来: 之, 经, 季, 是, 放, 翠, ，, 一,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 障, 季, 是, 粤, 四, 隋, 掩,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 异, 。, 长, 是, 障, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 异, ）, \n",
      ", 经, 是, 。, 映, 掩,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 是, 经, 四, 不, 一, 当, （, 潺,\n",
      "Average loss at step  162000 :  0.9898730531334877\n",
      "Average loss at step  164000 :  0.9912687926590442\n",
      "Average loss at step  166000 :  0.9974720120429993\n",
      "Average loss at step  168000 :  0.9887512648403645\n",
      "Average loss at step  170000 :  0.999083775639534\n",
      "Nearest to 来: 经, 之, 季, 是, 翠, ，, 一, 隋,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 障, 季, 是, 。, 粤, 四, 隋,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: \n",
      ", 几, 。, 异, 长, 是, 障, 别,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: \n",
      ", 异, ）, 经, 。, 是, 映, 当,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, 四, \n",
      ", 不, 当, 一, 映,\n",
      "Average loss at step  172000 :  0.9971202568113804\n",
      "Average loss at step  174000 :  0.9900529750287533\n",
      "Average loss at step  176000 :  0.9996395184397697\n",
      "Average loss at step  178000 :  0.9951504143476486\n",
      "Average loss at step  180000 :  0.9968540988564492\n",
      "Nearest to 来: 经, 之, 季, 一, 是, 翠, 放, ，,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 障, 季, 是, 粤, 四, 柳, 隋,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: \n",
      ", 几, 异, 长, 。, 是, 障, 柳,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 异, ）, \n",
      ", 经, 是, 。, 当, 映,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, 四, 一, 当, 不, \n",
      ", 之,\n",
      "Average loss at step  182000 :  0.9887803035378456\n",
      "Average loss at step  184000 :  0.9912940081059932\n",
      "Average loss at step  186000 :  0.9943869217336178\n",
      "Average loss at step  188000 :  0.9922290369570256\n",
      "Average loss at step  190000 :  0.9953781957030297\n",
      "Nearest to 来: 经, 之, 季, 一, 翠, ，, 是, 放,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 障, 季, 是, 粤, 。, 隋, 柳,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: \n",
      ", 几, 长, 异, 。, 是, 障, 柳,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 异, ）, \n",
      ", 经, 。, 是, 映, 当,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, \n",
      ", 四, 一, 不, 当, 几,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average loss at step  192000 :  0.9905284448862076\n",
      "Average loss at step  194000 :  0.9980870316028595\n",
      "Average loss at step  196000 :  0.990584470897913\n",
      "Average loss at step  198000 :  0.9961720753312111\n",
      "Average loss at step  200000 :  0.989284222304821\n",
      "Nearest to 来: 经, 之, 季, 一, 是, 翠, ，, 隋,\n",
      "Nearest to 事: 宕, 铁, 矛, 懋, 计, 投, 袂, 黻,\n",
      "Nearest to 寒: 弗, 阜, 纲, 咳, 庠, 滟, 嘲, 瓢,\n",
      "Nearest to 水: 潺, 障, 季, 是, 粤, 隋, 。, 四,\n",
      "Nearest to 与: 仙, 塔, 灼, 劚, 捋, 量, 啮, 颙,\n",
      "Nearest to 歌: 潦, 兴, 茵, 级, 兹, 卓, 菊, 仝,\n",
      "Nearest to 深: 燥, 队, 煌, 逅, 脍, 住, 瞻, 未,\n",
      "Nearest to 尽: 驮, 殿, 鬒, 隐, 陌, 箨, 宏, 麈,\n",
      "Nearest to ）: 几, \n",
      ", 。, 长, 异, 是, 障, 柳,\n",
      "Nearest to 江: 敧, 戎, 推, 晒, 铎, 态, 淰, 沁,\n",
      "Nearest to 长: 异, ）, \n",
      ", 经, 。, 是, 映, 当,\n",
      "Nearest to 空: 醿, 萏, 燧, 斫, 跻, 穟, 盏, 叇,\n",
      "Nearest to 梦: 靶, 环, 籍, 租, 探, 槌, 饶, 价,\n",
      "Nearest to 金: 於, 糯, 镝, 溱, 堞, 柝, 拭, 搂,\n",
      "Nearest to 有: 轶, 顿, 轧, 萝, 胚, 局, 妾, 苓,\n",
      "Nearest to 明: 经, 是, 四, 当, 几, 一, 不, 隋,\n"
     ]
    }
   ],
   "source": [
    "# Step 5: Begin training.\n",
    "# 开始训练\n",
    "num_steps = 200001\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    init.run()\n",
    "    print('Initialized')\n",
    "\n",
    "    average_loss = 0\n",
    "    for step in range(num_steps): \n",
    "        batch_inputs, batch_labels = batch,labels \n",
    "        feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}\n",
    "\n",
    "        _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict) \n",
    "        average_loss += loss_val \n",
    "\n",
    "        if step % 2000 == 0:\n",
    "            if step > 0:\n",
    "                average_loss /= 2000\n",
    "            print('Average loss at step ', step, ': ', average_loss)\n",
    "            average_loss = 0\n",
    "\n",
    "        if step % 10000 == 0:  \n",
    "            sim = similarity.eval()\n",
    "            for i in range(valid_size): \n",
    "                valid_word = reversed_dictionary[valid_examples[i]]\n",
    "                top_k = 8  \n",
    "                nearest = (-sim[i, :]).argsort()[1:top_k + 1]\n",
    "                log_str = 'Nearest to %s:' % valid_word             \n",
    "                 \n",
    "                for k in range(top_k):\n",
    "                    close_word = reversed_dictionary[nearest[k]]\n",
    "                    log_str = '%s %s,' % (log_str, close_word)\n",
    "                print(log_str)\n",
    "                \n",
    "    final_embeddings = normalized_embeddings.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 6: Visualize the embeddings.\n",
    "\n",
    "\n",
    "# pylint: disable=missing-docstring\n",
    "# Function to draw visualization of distance between embeddings.\n",
    "def plot_with_labels(low_dim_embs, labels, filename):\n",
    "  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'\n",
    "  plt.figure(figsize=(18, 18))  # in inches\n",
    "  for i, label in enumerate(labels):\n",
    "    x, y = low_dim_embs[i, :]\n",
    "    plt.scatter(x, y)\n",
    "    plt.annotate(label,\n",
    "                 xy=(x, y),\n",
    "                 xytext=(5, 2),\n",
    "                 textcoords='offset points',\n",
    "                 ha='right',\n",
    "                 va='bottom')\n",
    "\n",
    "  plt.savefig(filename)\n",
    "\n",
    "try:\n",
    "  # pylint: disable=g-import-not-at-top\n",
    "  from sklearn.manifold import TSNE\n",
    "  import matplotlib.pyplot as plt\n",
    "\n",
    "  tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')\n",
    "  plot_only = 500\n",
    "  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])\n",
    "  labels = [reversed_dictionary[i] for i in xrange(plot_only)]\n",
    "  plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))\n",
    "\n",
    "except ImportError as ex:\n",
    "  print('Please install sklearn, matplotlib, and scipy to show embeddings.')\n",
    "  print(ex)\n",
    "\n",
    "np.save('embedding.npy', final_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "import collections\n",
    "import json\n",
    "with open(\"./dictionary.json\",\"w\",encoding='utf-8') as f:\n",
    "    json.dump(dictionary,f)\n",
    "\n",
    "with open(\"./reverse_dictionary.json\",\"w\",encoding='utf-8') as f:\n",
    "    json.dump(reversed_dictionary,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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